AI in neuromodulation: recent advances

Neuromodulation offers promising interventions for a variety of neurological and psychiatric disorders and helps us understand how the brain works. With the advent of artificial intelligence (AI), the landscape of neuromodulation is experiencing a paradigm shift. AI's capability to analyze vast datasets, discern patterns, and make predictive analyses offers remarkable potential in optimizing neuromodulatory therapies, personalizing treatment protocols, and understanding the complex neural mechanisms underlying brain function and dysfunction.

This Research Topic aims to explore the intersection of AI and neuromodulation, showcasing recent advances, innovations, and future directions in this interdisciplinary field. By integrating AI technologies with neuromodulatory techniques, researchers and clinicians can enhance the precision, efficacy, and outcomes of interventions. We seek to bring together a collection of cutting-edge research that underscores the transformative role of AI in neuromodulation, from theoretical and computational perspectives to experimental and clinical applications.

We invite contributions that cover, but are not limited to, the following areas:

1. Data-Driven Neuromodulation: Machine learning algorithms for analyzing neural datasets, predictive models for patient-specific neuromodulation therapies.

2. Clinical Applications: AI-enhanced approaches for treating neurological disorders, the role of AI in psychiatric neuromodulation and case studies and clinical trials showcasing AI-integrated neuromodulation treatments.

3. Technological Innovations: Development of AI-powered neuromodulatory devices.

4. Neural Mechanisms and Insights: Utilizing AI to decipher the neural mechanisms underlying neuromodulation effects, AI-driven insights into neuroplasticity and circuit dynamics, Computational models simulating the impact of neuromodulatory interventions.

5. Ethical and Societal Implications: Ethical considerations in AI-driven neuromodulation therapies, Societal impact and accessibility of AI-enhanced neuromodulation.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: AI, neuromodulation, machine-learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.